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[P] PyTorch Implementation of Semantic Segmentation models

Nothing fancy, but to get a handle of semantic segmentation methods, I re-implemented some well known models with a clear structured code (following this PyTorch template), in particularly:

  • The implemented models are: Deeplab V3+ – GCN – PSPnet – Unet – Segnet and FCN

  • Supported datasets: Pascal Voc, Cityscapes, ADE20K, COCO stuff,

  • Losses: Dice-Loss, CE Dice loss, Focal Loss and Lovasz Softmax,

with various data augmentations and learning rate schedulers (poly learning rate and one cycle).

I though I share this implementation in case anyone might be interested, and here it is :

Github: https://github.com/yassouali/pytorch_segmentation

submitted by /u/youali
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.